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Artificial Intelligence in Rheumatology: A Comprehensive Bibliometric Analysis and Current Scientific Mapping

Maria Polyzou^1,2, Johanna Mucke^2,3, Anna Kernder²

¹Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece.

Mediterranean Journal of Rheumatology

|June 29, 2026

Summary

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This summary is machine-generated.

This bibliometric analysis maps Artificial Intelligence (AI) in Rheumatology, revealing key research trends and collaborations. It highlights AI

Area of Science:

Bibliometrics
Rheumatology
Artificial Intelligence

Background:

Limited comprehensive overviews exist on Artificial Intelligence (AI) applications in Rheumatology.
Bibliometric analysis offers a robust method to map research landscapes and identify trends.

Purpose of the Study:

To provide a holistic overview of AI research trends and clusters in Rheumatology.
To identify key collaborative networks and thematic areas within AI in Rheumatology.

Main Methods:

Bibliometric techniques including citation analysis, co-authorship analysis, co-occurrence analysis, and bibliographic coupling.
Analysis of scientific literature to identify research patterns and connections.

Main Results:

Keywords:

VOS viewer artificial intelligence large language models machine learning osteoarthritis rheumatic diseases rheumatoid arthritis rheumatology spondyloarthritis

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Citation analysis identified highly cited papers, indicating diverse research dimensions.

Co-authorship analysis revealed distinct international collaboration clusters (European, USA, Asian-Pacific).

Co-occurrence analysis identified four thematic clusters: rheumatic diseases, prediction methods, AI algorithms, and patient characteristics.

Conclusions:

The study provides valuable insights into the evolving use of AI in Rheumatology.
Identified trends and collaborations can guide future research directions.
Acknowledges limitations such as keyword bias and database restrictions.